Marine Ecology Skills
Marine Ecology Skills 2023-24
School of Ocean Sciences
Module - Semester 1
The module provides an introduction to the scientific method and an overview of approaches to experimental and survey sampling design, data analysis and interpretation and report writing. Learning will be enhanced through ship-based sampling and laboratory analysis of samples, and students will collaborate to produce a dataset which they will independently analyse to practice statistical and data communication skills. Students will learn about and practice scientific writing and develop introductory map making skills. More specifically the module includes:
- A description of the scientific process with a particular focus on null hypothesis significance testing and revising univariate statistical tests.
- Ideas surrounding statistical sampling design from an observational and empirical perspective
- Data exploration and univariate analysis in the statistical programming environment R
- Introduction to multivariate statistical methods commonly used by marine ecologists and working in the software Primer
- Ship-based benthic grab sampling
- Taxonomic identification of benthic organisms and laboratory processing to collaboratively produce a class dataset
- Independent analysis of the class dataset
- Scientific research and report writing skills
- Mapping and spatial analysis techniques in ArcGIS
-threshold --C (-50%>) A threshold student will have a basic knowledge of the scientific method and hypothesis driven framework, a basic ability to identify benthic organisms as well as a basic ability to quantitatively manipulate and investigate datasets using a range of fundamental approaches using appropriate computer software (R, Primer and ArcGIS). The student will be able to apply and interpret statistical tests and create an evidence based scientific reporting summarizing their work.
-good --B (60%>) A good student will have a thorough understanding of the scientific method and hypothesis driven framework, and a solid ability to identify benthic organisms as well as be competent in quantitatively manipulating datasets using a range of fundamental mathematical tools, have a good ability to interpret datasets and be able to confidently use and interpret statistical tests using appropriate computer software. The student will be able to concisely present and interpret their analytical findings in the context of the wider literature to create a compelling evidence based scientific report summarizing their work.
-excellent --A (70%>) An excellent student will have a high-level understanding of the scientific method and ability to present testable hypotheses. They will have advanced benthic taxonomy identification skills, as well as have a sophisticated knowledge of quantitatively manipulating datasets using a range of fundamental mathematical tools. Their ability to interpret datasets will be advanced and they be highly skilled in the use and interpretation of statistical tests using appropriate computer software. The student will create a scientific report that has high level understanding of the ecological concepts, as well as a mature interpretation of the results presenting them in the context of wider literature. Their written and presentation style will be highly skilled, concise, clear and compelling.
- Students will be able to critically interpret the quantitative output of data analysis; to contextualise findings in light of current understanding, and to communicate the entirety of the research process as a formal experimental report.
- Students will be able to evaluate and apply the most appropriate statistical or spatial analysis technique for a range of experimental or survey scenarios, using a range of industry-standard software packages
- Students will be able to source and appraise the current knowledge and understanding base concerning the environmental drivers of variability in benthic community structure, and to use this in a written report
- Students will have shown mastery of field-based research approaches by: setting testable hypotheses; designing and conducting field survey sample collection; developing specialist taxonomic identification and description abilities for marine benthos; and analysing the data using appropriate quantitative techniques.
- Students will show knowledge of applying GIS principles to spatial analysis outputs using industry-standard software; develop predictive species distribution models from species and environmental data using geospatial techniques; summarise and communicate research outputs to inform a commercial marine sector problem in the form of an executive report.
A scientific report (standard IMRAD structure) outlining an investigation into the effect of depth and other environmental drivers on benthic community composition
Predictive modelling GIS Exercise